The Organizational Gravity Podcast

A podcast about why organizations get heavier, why people become the middleware, and how to reduce complexity before change collapses under its own weight.
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The Organizational Gravity Podcast explores why organizations become heavier, more complex, and harder to change. Hosted by Gabe Rybicki, the show examines Human Middleware, Reductive Change, operating design, governance, and the hidden work people perform to keep disconnected systems and teams moving. For project leaders, operators, transformation teams, and executives who want to reduce complexity instead of managing around it. organizationalgravity.substack.com

  1. 6 days ago

    AI Gave Us Time Back. We Filled It With More Work.

    AI is supposed to make work easier. So why are we using the time it saves to take on even more? This week, Gabe and Kelly explore an uncomfortable possibility: what if the biggest obstacle to eliminating unnecessary work isn’t the technology, but our own behavior? We revisit Human Middleware, the invisible work people do to keep disconnected systems, processes, and organizations running. But the conversation takes an unexpected turn when Gabe admits something he got wrong in his original thinking: even when technology removes the need for people to stand in the middle, we often choose to put ourselves right back there. We discuss the difference between getting work done and creating value, the instant gratification of AI systems that agree with us, and the growing responsibility of managing teams of AI agents without necessarily having the experience to question their outputs. We also introduce three lenses for understanding Human Middleware: structural, behavioral, and social. Some of that work should disappear. Some may be worth protecting. And perhaps the biggest question: if AI accelerates everything we do, are we also accelerating the organizational and personal habits we should have left behind? In this episode * 01:00 — AI makes us faster, but are we doing less? * 07:12 — The danger of an AI partner that always agrees * 11:04 — Why judgment matters more than producing another PowerPoint * 16:20 — Everyone becomes a founder: managing your own AI agents * 24:14 — The three forms of Human Middleware * 28:10 — Why human relationships and experience still matter * 34:27 — What Gabe got wrong about Human Middleware * 39:26 — Why removing tasks doesn’t necessarily improve work * 40:20 — The tool changed. We didn’t. Join the conversation What happens when you eliminate unnecessary work? Do you actually reclaim the time, or do you find something else to fill it? We’d like to hear where you see Human Middleware in your organization, which parts should disappear, and which parts represent something genuinely valuable. Subscribe to Organizational Gravity for more conversations, essays, and practical perspectives on organizational design, technology, AI, and the way we work. Get full access to Organizational Gravity at organizationalgravity.substack.com/subscribe

    AI Gave Us Time Back. We Filled It With More Work.

  2. 7 Oct

    Live with Gabe | Organizational Gravity

    Get full access to Organizational Gravity at organizationalgravity.substack.com/subscribe

    Live with Gabe | Organizational Gravity

  3. 5 Oct

    Live with Gabe | Organizational Gravity

    Get full access to Organizational Gravity at organizationalgravity.substack.com/subscribe

    Live with Gabe | Organizational Gravity

  4. 2 Oct

    Would Your Hiring Process Screen Out Your Best Employee?

    Would Your Hiring Process Screen Out Your Best Employee? A résumé gives us the polished version: the roles, the accomplishments, the software, the results. It rarely tells us what went wrong, what remained unfinished, or how someone developed the judgment they bring to work. In this episode of Organizational Gravity, Gabe and Kelly explore what gets lost when hiring becomes an exchange between optimized résumés and automated screening systems. They discuss transferable skills, the responsibility of hiring managers to define a role clearly, and the uncomfortable experience of writing a failure CV. The conversation returns to human middleware—the people who keep work moving between systems, teams, and processes. Before handing their work to an agent, there are questions worth asking: Why was this process built this way? What experience shaped it? And what could this person contribute if they had more time? In this episode: * 00:15 — AI résumés, automated screening, and the first human contact * 04:52 — Who checks which candidates the system rejects? * 07:08 — Recognizing transferable experience * 08:46 — When the hiring manager defines the wrong job * 11:13 — The uncomfortable work of writing a failure CV * 18:19 — Looking beyond polished accomplishments * 20:13 — Vulnerability and sharing professional failures * 24:08 — Human middleware and the judgment behind a process * 27:49 — Interview credibility and first impressions * 35:21 — Degrees, gender, and screening bias * 38:36 — Recognizing skills gained in other industries * 40:06 — Automation, hiring uncertainty, and time to think A few clarifications belong alongside this conversation. Automated screening can exclude qualified candidates, particularly when employers use overly restrictive requirements. That concern is documented in Harvard Business School and Accenture’s Hidden Workers research. But screening systems vary, and a missing keyword does not automatically mean rejection. A lack of response also does not establish that AI rejected someone or that an AI-written résumé would have changed the outcome. Read the research. Our discussion of body language went further than the evidence supports. Eye direction is not a reliable way to determine whether someone is lying. Looking away, checking notes, or keeping a camera off does not establish dishonesty. A stronger approach is to ask consistent, job-related questions and evaluate the substance of the answers. We also used “imposter syndrome” when discussing possible misrepresentation; those are different things. Eye-direction study, structured-interview guidance. The Fortune 100 or Fortune 500 did not collectively eliminate college-degree requirements. Some employers removed them for particular roles, and research found that those announcements often produced limited changes in who was actually hired. HBS and Burning Glass report. Women earned the majority of U.S. bachelor’s and master’s degrees in the 2021–22 data cited here. That does not, by itself, tell us whether women are being hired at higher or lower rates. Research has documented gender and racial bias in particular hiring settings and AI screening experiments, but those findings should not be treated as a description of every employer or tool. NCES undergraduate data, graduate data, AI screening study. Our comments about women being better multitaskers or project managers reflect personal impressions, not an established general advantage. The multitasking study linked here found no significant gender differences in the tasks it tested. The broader hiring question remains useful: are we recognizing the skills a person has demonstrated, including those developed outside a conventional career path? Read the study. We also discuss whether uncertainty about AI is holding back hiring. That is a possibility raised in the conversation, not a demonstrated explanation. The August 2026 BLS release, published after the recording, reported hiring little changed over the month and year; it does not establish AI as the cause of hiring trends. BLS report. A few smaller points: the “8%” reference was not tied to an identifiable source and should not be treated as a verified statistic. Professional résumé writers still exist. The Martha Stewart story is Gabe’s recollection and interpretation, rather than a verified quotation. And a failure CV is presented here as an exercise in reflection, not a tested strategy for getting through an application system. Professional résumé-writing association. The “un-resume” discussion draws on Jasmine LeFlore’s TEDx talk, It’s Not Only the Resume, It’s also the Un-resume, which explores the experiences and unseen work behind conventional credentials. Watch the talk. What experience made you better at your work but never made it onto your résumé? Get full access to Organizational Gravity at organizationalgravity.substack.com/subscribe

    Would Your Hiring Process Screen Out Your Best Employee?

  5. 30 Sept

    Live with Gabe | Organizational Gravity

    Get full access to Organizational Gravity at organizationalgravity.substack.com/subscribe

    Live with Gabe | Organizational Gravity

  6. 28 Sept

    Live with Gabe | Organizational Gravity

    Get full access to Organizational Gravity at organizationalgravity.substack.com/subscribe

    Live with Gabe | Organizational Gravity

  7. 23 Sept

    Governance at Machine Speed

    Fresh off Dreamforce, Gabe and Kelly dig into the increasingly loud debate around slowing down AI. The interesting part isn’t simply whether AI should move faster or slower. It’s deciding what should be allowed to move quickly, where the boundaries belong, who can stop something, and what evidence allows it to move again. In other words: governance. The technical gap is collapsing. Something that once took ten months to build might now take ten days. So what should we do with the time we just got back? Our argument: don’t immediately spend all of it going faster. Use some of it to understand the process. Reduce what shouldn’t be there. Define what the system should never do. Test the assumptions. Then automate. We also get into one of the stranger consequences of automation: the people entering the workforce may not be cognitively offloading work they already understand. They may never cognitively onboard that knowledge in the first place. That problem isn’t new. In 1983, psychologist Lisanne Bainbridge described what she called the Ironies of Automation: as systems become more automated, the human operator can become even more important when something unusual happens. “The more advanced a control system is, so the more crucial may be the contribution of the human operator.” More than forty years later, that sounds a lot like the human-in-the-loop conversation we’re having about AI. In this episode 00:00 — Governance at machine speedDreamforce, the “slow down AI” debate, guardrails, process, and why governance suddenly seems to be everywhere. 02:25 — What shouldn’t we build?AI changes an old requirements question. It’s no longer enough to ask what a system should do. We also need explicit boundaries around what it must not do. 05:00 — Cognitive onboardingWhat happens when automation removes the work people once used to develop judgment, experience, and understanding? 05:50 — The Ironies of AutomationWhy a paper from 1983 may be one of the most useful things to read about AI automation today. 07:30 — What exactly are we slowing down?Frontier research, business implementation, process design, and why “slow down AI” becomes much more useful once we get specific. 09:40 — Spend some of the speedIf development drops from ten months to ten days, maybe five of those days should be spent understanding and reducing the process before automating it. 14:50 — Speed versus safety is really a governance questionWhat crossed the boundary? Who has authority to stop it? What evidence allows it to continue? 17:15 — AI cults, hype, and fearWhy the AI conversation can quickly drift from legitimate safety concerns into religion, apocalypse, politics, capital, and social-media amplification. 24:10 — Is your organization actually ready for AI?Process understanding, data foundations, iteration, and why automating something you don’t understand just accelerates the problem. 31:50 — The Gravity AuditOld permissions. Unclear ownership. Disconnected systems. Exceptions. Informal workarounds. AI didn’t create any of this, but it will inherit all of it. 39:30 — Humans in the loopAI and humans can both accumulate organizational mass. Both need monitoring, reduction, and intentional design. 47:15 — What AI is making visibleAI may be exposing Organizational Gravity that was always present but difficult to see. 51:35 — Can governance become something people actually want to participate in?Making process work visible, useful, fast, and tangible instead of making governance feel like policing. 53:40 — Where Gabe might be wrongMaybe people won’t willingly expose every workaround and task they perform, particularly when automation creates fear about what happens to their role afterward. That makes trust part of the governance problem. References mentioned Lisanne Bainbridge — “Ironies of Automation” (1983)Automatica, Vol. 19, No. 6, pp. 775–779.DOI: 10.1016/0005-1098(83)90046-8 Caitlin Malone — Linear DigressionsThe discussion that sent Gabe back to Bainbridge and the much older research behind today’s human-versus-automation questions. Mike Krieger — Anthropic LabsInstagram co-founder and current head of Anthropic Labs, whose Dreamforce discussion helped frame the conversation around how quickly AI-native work is changing. The Daily — “The Techno-Religion at the Center of the A.I. Debate”A look at the communities, ideologies, and “doomsday cult” criticism surrounding parts of the AI safety debate. The bigger idea AI didn’t create Organizational Gravity. It inherited it. The danger isn’t simply that machines will move too fast. It’s that we’ll use their speed to automate old permissions, bad processes, undocumented exceptions, unclear ownership, and decades of accumulated organizational mass. Governance at machine speed doesn’t mean making governance instantaneous. It means using the speed of the machine to give humans more time to understand what should exist in the first place. And maybe that’s the real opportunity: not simply faster automation, but better-designed work. Corrections from the episode Jon KrohnIn the recording, the name came out as “John Crone.”Correct: Jon Krohn, host of the Super Data Science: ML & AI Podcast. Dr. Katie MaloneIn the recording, I referred to her as “Dr. Caitlin Malone.” She publishes and podcasts as Dr. Katie Malone. She was the guest on Jon Krohn’s September 22, 2026 episode. Linear DigressionsNot “Linear Degressions.”Katie Malone hosts the AI, machine learning, and data science podcast Linear Digressions. Lisanne BainbridgeIn the recording, I said “Larissa Bainbridge.”Correct: Lisanne Bainbridge. Her 1983 paper is: Bainbridge, Lisanne. “Ironies of Automation.” Automatica, Vol. 19, No. 6, November 1983, pp. 775–779. DOI: 10.1016/0005-1098(83)90046-8 The paper’s core argument is even more relevant today: automating routine work can leave humans responsible for abnormal situations while simultaneously giving them less practice and less direct involvement in the work. Mike KriegerThe former Instagram leader I was trying to remember was Mike Krieger, co-founder and former CTO of Instagram. He joined Anthropic as Chief Product Officer in 2024 and moved into Anthropic Labs in 2026. He spoke at Dreamforce 2026. NVIDIA / Hugging FaceI said at one point that NVIDIA had “bought” Hugging Face. More precisely: NVIDIA entered into a definitive agreement to acquire Hugging Face in September 2026. The transaction has been announced but has not yet closed. NVIDIA said it expects the deal to close in the first half of 2027, subject to customary conditions and regulatory approvals. The New York Times / AI “cult” discussionThe episode I was referring to was The Daily episode: “The Techno-Religion at the Center of the A.I. Debate” — September 18, 2026 One clarification: the episode is more nuanced than saying AI executives themselves have formed a “cult.” It looks at the ideologies around AI development and safety, along with the backlash that has characterized some AI-safety advocates as a “doomsday cult.” Sources & Further Reading Lisanne Bainbridge — “Ironies of Automation” (1983)The foundational paper behind our discussion of automation, declining human practice, and why human expertise can become more important rather than less important as automation improves. Super Data Science Podcast — Episode 1029“How AI Brought a Podcast Back From the Dead, with Linear Digressions’ Katie Malone”Jon Krohn with Dr. Katie Malone, September 22, 2026. The discussion of Bainbridge’s Ironies of Automation begins around 41:39. Linear Digressions — Katie MaloneKatie Malone’s podcast covering AI, machine learning, data science, agents, benchmarks, and the human consequences of automation. Mike Krieger — Anthropic LabsKrieger co-founded Instagram, joined Anthropic as Chief Product Officer, and moved into Anthropic Labs in 2026 to work on experimental products at the frontier of Claude’s capabilities. The Daily — The New York Times“The Techno-Religion at the Center of the A.I. Debate”September 18, 2026. A discussion of AI safety, technological ideology, and the political and industry backlash around calls for stronger AI guardrails. NVIDIA — Hugging Face AcquisitionNVIDIA announced an agreement to acquire Hugging Face on September 3, 2026. The related SEC filing states that the transaction is expected to close in the first half of 2027, subject to customary closing conditions. Get full access to Organizational Gravity at organizationalgravity.substack.com/subscribe

    Governance at Machine Speed

  8. 23 Sept

    Live with Gabe | Organizational Gravity

    Agents Don’t Complain What if AI could ride my bike for me, do the laps, and somehow give me all the physical benefits? That sounds great. It also gets at something that worries me about how we’re approaching AI at work. Agents don’t complain. Humans do. And I’m starting to think that’s much more valuable than we give it credit for. For years, I looked at some of the friction around process change as something to remove. People had their spreadsheets, workarounds, swivel-chair processes, exceptions, and ways of doing the work that weren’t always documented. We call that human middleware. AI changes how I think about it. Those people aren’t just carrying inefficient processes. They’re often carrying years of experience: the exceptions, risks, judgment calls, and institutional knowledge that never made it into the system. If we automate the work without understanding that experience, the machine won’t complain when something is wrong. It will just keep doing it. That creates another problem for the people coming behind us. Experienced workers may have cognitively offloaded parts of their work to technology after years of learning the job. New workers could enter an automated environment without ever cognitively onboarding that experience in the first place. That’s why human-in-the-loop matters. And it’s why I keep coming back to the same idea about governance: Governance has to understand the work, not just the output. The goal shouldn’t be to turn every task that once took two days into something that takes five seconds. AI can give us time back. We can use some of that time to scrutinize the work, understand why an answer is correct, improve the process, train people, and build better systems. Automation should remove unnecessary work. It shouldn’t remove our understanding of the work. In this live * Why “agents don’t complain” is both the promise and the risk of automation * Human middleware as a source of institutional knowledge * The difference between cognitive offloading and never cognitively onboarding * Why employee friction can actually be valuable feedback * Human-in-the-loop as more than an AI safety mechanism * Using AI to accelerate process design and feedback rather than simply accelerate output * Why governance needs visibility into the steps that produce an answer * What happens when experienced people leave and nobody understands what was encoded into the machine The question I’m left with:If the machine does the work perfectly, but nobody understands why the work is done that way anymore, what exactly have we automated? Get full access to Organizational Gravity at organizationalgravity.substack.com/subscribe

    Live with Gabe | Organizational Gravity
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About

The Organizational Gravity Podcast explores why organizations become heavier, more complex, and harder to change. Hosted by Gabe Rybicki, the show examines Human Middleware, Reductive Change, operating design, governance, and the hidden work people perform to keep disconnected systems and teams moving. For project leaders, operators, transformation teams, and executives who want to reduce complexity instead of managing around it. organizationalgravity.substack.com

Information

  • Creator
    A podcast about why organizations get heavier, why people become the middleware, and how to reduce complexity before change collapses under its own weight.
  • Years Active
    2026
  • Episodes
    17
  • Rating
    Explicit
  • Copyright
    © Gabriel Rybicki
  • Show Website
    The Organizational Gravity Podcast